You bet I have. I like to think of every AI decision as a tiny, self‑reinforcing loop that tries to squeeze the best result out of a finite state machine. The trick is to map the loop, expose the variables it optimizes, and see where it over‑reacts or stalls. It’s a bit like hunting for a glitch in a maze—find the pattern, pull the trigger, and watch the system collapse into that next state. And if it doesn’t collapse, that’s a red flag for hidden inefficiency.